System, method, and recording medium for determining and discerning items with multiple meanings

ABSTRACT

A method, system, and non-transitory compute readable medium determining and discerning items with multiple meanings in a sequence of items including producing a distributed representation for each item of the sequence of items including a word vector and a context vector, partitioning the sequence of items into classes, for an item using a representative word vector of each class, calculating a cosine distance between the word vector of said item and the class representative vector, and producing a new sequence of items by modifying the distributed representation in the producing by replacing each occurrence of an item depending on the cosine distance calculated by the calculating.

BACKGROUND

The present invention relates generally to determining and discerningitems with multiple meanings, and more particularly, but not by way oflimitation, to an unsupervised and non-dictionary based system, method,and recording medium for determining “senses” in which a word appearsand associating the words with particular occurrences in the inputsequence.

Conventional techniques of determining and discerning items withmultiple meanings of words have proven useful in applications such asdiscerning multiple meanings. Generally, success depends on usingexternal resources such as dictionaries, thesauri and user input. Todate, distributed representations have not been used in discerningmeanings although they proved useful in other domains such as analogydetermination. However, despite many usages suggested for word vectors,their internal structure remains opaque. That is, the conventionaldistributed determining and discerning items with multiple meanings donot make possible to discern between words/items with multiple meaningswith any reliability simply because it is unknown how to decode vectorentries in distributed representations.

Conventional techniques of determining and discerning items withmultiple meanings systems cannot discern items with multiple meaningswith high reliability without external resources. For example, the word‘party’ may appear in a sequence. By examining the distributedrepresentation of words in the sequence, conventional systems cannotdiscern between the multiple meanings of the word “party” which couldmean a celebration, a group of people, a political association, etc.

Discerning the different meanings of words in a corpus has been thesubject of much research. Conventional methods have proposed anunsupervised method based on word vectors to discriminate between sensesof a word, without labeling (or explaining) these senses. The methodinvolves no learning although it suggests using SVD decomposition toreduce vectors' dimensionality. Each word w is associated with a wordvector (different than the ones used in this patent) whose dimensionsare words occurring in a window around the word w. The vector entry in adimension is the number of occurrences of the word of this dimension inthe windows around occurrences of w in the learning text. Given aparticular word w to be disambiguated, one forms context vectors for wover the training text. A context vector is simply the normalizedaverage vector of the word vectors of words in a window around anoccurrence of w. The collection of context vectors for w is partitionedinto clusters and the average vector of each cluster represents thecluster. Given a w occurrence in some test text, a context vector c forthis occurrence of w is constructed. The cluster whose representativevector is cosine-closest to c defines the sense of this “new” woccurrence in the test text.

Thus, there is a technical problem in the conventional determining anddiscerning items, using distributed representation, with multiplemeanings systems as they have no capability to discern between wordshaving multiple meanings with an explanation of said meanings. Morespecifically, the conventional methods have the technical problem thatthe conventional methods do not have two trainings, one over theoriginal text and one over the modified text in which each word isreplaced by its “sense” so as to explain the ‘senses’ using the textitself. Further, the conventional methods do not use a class averagecontext vectors as trained in this application as such vectors are notpresent in the conventional techniques. Accordingly, the conventionalsystems cannot help a user to comprehend the sequence even without adictionary.

SUMMARY

In an exemplary embodiment, the present invention can provide a methodof determining and discerning items with multiple meanings in a sequenceof items, the method including producing a distributed representationfor each item of the sequence of items including a word vector and acontext vector, partitioning the sequence of items into classes, for anitem using a representative word vector of each class, calculating acosine distance between the word vector of said item and the classrepresentative vector, and producing a new sequence of items bymodifying the distributed representation in the producing by replacingeach occurrence of an item depending on the cosine distance calculatedby the calculating.

Further, in another exemplary embodiment, the present invention canprovide a non-transitory computer-readable recording medium recording aprogram for determining and discerning items with multiple meanings in asequence of items, the program causing a computer to perform: producinga distributed representation for each item of the sequence of itemsincluding a word vector and a context vector, partitioning the sequenceof items into classes, using a representative word vector of each class,calculating a cosine distance between the word vector and the classrepresentative word vector, and producing a new sequence of items bymodifying the distributed representation in the producing by replacingeach occurrence of an item depending on the cosine distance calculatedby the calculating.

Even further, in another exemplary embodiment, the present invention canprovide a system for determining and discerning items with multiplemeanings in a sequence of items, the system including a firstdistribution producing device configured to produce a distributedrepresentation for each item of the sequence of items including a wordvector and a context vector, a first partitioning device configured topartition the sequence of items into classes, a closeness determiningdevice configured to calculate, using a representative word vector ofeach class, a cosine distance between the word vector and the classrepresentative word vector, and a new sequence producing deviceconfigured to produce a new sequence of items by replacing eachoccurrence of an item depending on the cosine distance calculated by thecloseness determining device.

There has thus been outlined, rather broadly, an embodiment of theinvention in order that the detailed description thereof herein may bebetter understood, and in order that the present contribution to the artmay be better appreciated. There are, of course, additional exemplaryembodiments of the invention that will be described below and which willform the subject matter of the claims appended hereto.

It is to be understood that the invention is not limited in itsapplication to the details of construction and to the arrangements ofthe components set forth in the following description or illustrated inthe drawings. The invention is capable of embodiments in addition tothose described and of being practiced and carried out in various ways.Also, it is to be understood that the phraseology and terminologyemployed herein, as well as the abstract, are for the purpose ofdescription and should not be regarded as limiting.

As such, those skilled in the art will appreciate that the conceptionupon which this disclosure is based may readily be utilized as a basisfor the designing of other structures, methods and systems for carryingout the several purposes of the present invention. It is important,therefore, that the claims be regarded as including such equivalentconstructions insofar as they do not depart from the spirit and scope ofthe present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The exemplary aspects of the invention will be better understood fromthe following detailed description of the exemplary embodiments of theinvention with reference to the drawings.

FIG. 1 exemplarily shows a block diagram illustrating a configuration ofa system 100 determining and discerning items with multiple meanings.

FIG. 2 exemplarily shows a high level flow chart for determining anddiscerning items with multiple meanings.

FIG. 3 exemplary shows a sequence before and after producing a seconddistribution representation 205.

FIG. 4 exemplary shows a final output by a display device 107.

FIG. 5 depicts a cloud computing node according to an embodiment of thepresent invention.

FIG. 6 depicts a cloud computing environment according to anotherembodiment of the present invention.

FIG. 7 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

The invention will now be described with reference to FIGS. 1-7, inwhich like reference numerals refer to like parts throughout. It isemphasized that, according to common practice, the various features ofthe drawing are not necessary to scale. On the contrary, the dimensionsof the various features can be arbitrarily expanded or reduced forclarity. Exemplary embodiments are provided below for illustrationpurposes and do not limit the claims.

It should be noted that from here on the term “word” or “item” standsfor either a word, a concept (e.g., a Wikipedia entry, and in general asequence of words that is identified as a concept by a community, e.g.“French revolution”), an item that appears in a sequence (e.g., asequence of graph nodes, a sequence of chemical formulae), etc.

With reference now to FIG. 1, the determining and discerning items withmultiple meanings system 100 includes a first distributionrepresentation producing device 101, a first partitioning device 102, acloseness determining device 103, a new sequence producing device 104, asecond distribution representation producing device 105, a secondpartitioning device 106, and a display device 107. The determining anddiscerning items with multiple meanings system 100 receives a sequenceof items 150 as an input.

It should be noted that “items” is intended to mean “words” but is notlimited thereto as any sequence of items can be input into thedetermining and discerning items with multiple meanings system 100.Thus, for example, the determining and discerning items with multiplemeanings system 100 includes a processor 180 and a memory 190, with thememory 190 storing instructions to cause the processor 180 to executeeach device of the determining and discerning items with multiplemeanings system 100.

Although as shown in FIGS. 5-7 and as described later, the computersystem/server 12 is exemplarily shown in cloud computing node 10 as ageneral-purpose computing device which may execute in a layer thedetermining and discerning items with multiple meanings systems 100(FIG. 7), it is noted that the present invention can be implementedoutside of the cloud environment.

The first distribution representation device 101 receives the sequenceof items 150 and produces a distributed representation for each item “w”as a word vector and a context vector. That is, the distributionrepresentation device 101 uses a vector producing algorithm such asword2vec (w2v) or Glove, and produces output vectors of two types pereach item: word (Syn0 type in w2v) and context (Syn1neg in w2v)). Morespecifically, given a sequence of items (words, concepts, nodes in agraph, etc. or a combination thereof), the first distributionrepresentation device 101 learns a distributed representation (i.e.vectors) of dimension n (a user specified parameter), using a tool suchas word2vec or Glove.

The first partitioning device 102 partitions the items into preferably aplurality of classes. For each class “C” the first partitioning device102 keeps a word representative vector “c” and a context representativevector “c”. The numbering of the classes that the partitioning device102 partitions the items into is C1 to Cm, where m is the number ofclasses. Generally, the first partitioning device 102 may partition theitems into, for example, 200 classes but the first partitioning device102 can partition the items into any number of classes that a userspecifies.

More specifically, the first partitioning device 102 partitions theitems into a number “m” of classes using the word vectors by usingK-means clustering or any other appropriate vector clustering methodscan be used. Each class is a set of items and classes are numberedarbitrarily as C1 to Cm. The union of the classes is the wholecollection of items or a subset of interest thereof. If an item “w”belongs to class Ci, the primary dimension of item “w” is “i”.

That is, the first partitioning device 102 forms “m” classes C1 to Cm,each class being a set of items. The union of the classes is the wholevocabulary or a portion thereof of interest. Class forming is done bythe first partitioning device 102 based on the distributedrepresentation, for example, by applying a clustering algorithm to theword vectors.

The first partitioning device 102 keeps the word (Syn0 in w2v) vectorsand context (Syn1neg in w2v) vectors. With each class C, the firstpartitioning device 102 keeps two representative vectors: “c”, derivedfrom the class word vectors, and “c”, derived from the class contextvectors. The representative vectors can be produced for a class byadding all the vectors in the class and producing a sum vector andnormalizing the sum vector by dividing the sum vector by its length.

Another exemplary way to produce a representative vector is to weigh theadded vectors by multiplying the word vector of item w by the number ofoccurrences of item w of the added vector of item w, or alternatively bymultiplying by the “log” of the number of occurrences of item w. Itshould be noted that there are additional methods for constructing theclass representative vector besides the ones mentioned above. Forexample, all vectors need not be used, but instead only those vectorscan be used with a number of occurrences between some constants a and bor between ranking a and b where ranking is in terms of the number ofoccurrences of the item in the input sequence. In all these methods, auser can choose an actual class member whose word vector is the closestto the calculated representative vector (e.g., by using cosinedistance).

For each word w, the closeness determining device 103 determines the kclasses that are closest to it. The “closeness” is calculated based on acosine distance to the c representative vectors of classes.

More specifically, using the representative word vector c (constructedout of the word vectors (Syn0 vectors in word2vec)) of each class C, thecloseness determining device 103 calculates the cosine distance betweenthe word vector of w and the class C representative, c, vector. In thismanner, the closeness determining device 103 determines the desired fewclasses, not including w's own class, that are “close” to item w. Thecloseness determining device 103 calculates the cosine distance for eachvocabulary item, each item w being associated with the few classes towhose representative word vector the cosine distance is highest, and forease of description the closeness determining device 103 assumes thatthere are always three such strongly relating classes which are numberedwith indexes “1”, “2” and “3”. It should be noted that there can be morethan 3 classes. The few classes are viewed as ‘senses’ of w as w isclose to these classes but not one of their members.

The new sequence producing device 104 produces a new sequence byreplacing each word w occurrence oc in the input sequence by w_i where iis the class among the k closest classes whose c′ vector representativeis cosine closest to the average context vector for the words in awindow around oc as shown in FIG. 3.

More specifically, the new sequence producing device 104 modifies theoriginal input sequence by replacing each occurrence of an item w with aw where s is 1, 2 or 3 depending on the cosine distance calculated bythe closeness determining device 103 between the particular stronglyrelating class (i.e., ‘sense’) context vector (Syn1neg type in w2v)representative (constructed similarly to its word (Syn0 type in w2v)representative) and a vector representing the context surrounding w inthe sequence.

The context surrounding w in the sequence is represented simply by theaverage vector av of word (Syn0 in w2v) vectors of the items (e.g., 8 oneach side) surrounding the occurrence of w in the input sequence. Thisdetermines to which context (Syn1neg in w2v) vector av, the vectorrepresenting the collection of surrounding items, is cosine closest.

Intuitively, this is the ‘sense’ in which the item w is used in thissurrounding context.

Note that the new sequence producing device 104 excludes the item itselffrom the context and just considers its surrounding sequence items. Thissubstitution has a side effect of expanding the vocabulary (it is notnecessarily three times the size of the original vocabulary as not allitem w senses w1, w2 and w3 are usually significant in practice). Itshould be noted that, in calculating vector av, different surroundingcontext items may be weighted differently, e.g., the closest may beweighted more.

The second distribution representation producing device 105 produces adistributed representation for the new sequence output by the newsequence producing device 104 for each word w with the “new” contextvector and the word vector.

The second partitioning device 106 partitions the new items intoclasses. That is, the second partitioning device 106 forms classes,using a clustering algorithm, based on the resulting word (Syn0 in w2v)vectors.

As shown in FIG. 4, the display device 107 displays dominant members ofeach formed class. For each original w, display its w_i variationsdominant members next to each other.

That is, the display device 107 displays for each original word w's“family” member w1, w2 and w3 the dominant representatives of its newlycomputed class for that member, in order to expose the differentmeanings, or senses, the member has in terms of the expanded vocabulary.

FIG. 2 shows a high level flow chart for a method 200 for determiningand discerning items with multiple meanings.

Step 201 receives a sequence of items 150 and produces a distributedrepresentation for each item “w” as a word vector and a context vector.Step 201 runs word2vec or similar vector producing tool and, obtainsword vectors v1, . . . , vn for words w1, . . . , wn in the vocabulary Vof text T=t1, t2, . . . , tu.

Step 202 partitions the items into classes. For each class “C”, step 202keeps a word representative vector “c” and a context representativevector “c”. Step 202 partitions the items in V into N classes, C1, . . ., CN, N about 10,000 based on the vectors obtained, e.g. by usingK-means or a similar procedure. For each class Ci, let its ci vector bethe class center vector of Ci derived from clustering the word (Syn0)vectors. Let ci′ be the class Ci center (average) derived from thecontext (Syn1neg) vectors of the items of class Ci. For each word wj inV, let Ci1, . . . , Cik be the “strong” classes whose cim, 1≦m≦k vectorsare closest to vj, preferably k should be such that k<8 and all ci2, . .. , cik are at least ⅔ as close to w as ci1 (i.e., the closest), is.

For each word w, step 203 determines the k classes that are closest toit by calculating a cosine distance to the c vectors of classes. Morespecifically, step 203 scans the text T=t1, t2, . . . , tV, let thecurrent center word be tm which, say, is a vocabulary word wi. Step 203considers the average of the word (Syn0) vectors of words in a window ofsize L, (e.g., 16 (8 on both sides)) surrounding word tm and then step203 calculates the value of the cosine distance of vector average andeach of c′i1, . . . , c′ik, let c′ij be the one for which the value ishighest. Index j is used in producing a replacement word for tm asfollows.

Step 204 produces a new sequence by replacing each word w occurrence ocin the input sequence by w_j where 1≦j≦k is the index of the class,among the k closest classes whose c′ vector is cosine closest to theaverage context vector for the words in a window around oc as shown inFIG. 3. More specifically, step 204 replaces in the text word occurrencetm with a “variation” wi_j. For example, if word occurrence tm=car and“car” has 3 “strong” classes and the current context (of tm) averagevector is closest to the vector c′i2 representing the second strongclass, then tm=car is replaced by car_2. So, each occurrence is replacedwith the appropriate “sense” of the word in the current window contextusage.

Step 205 produces a distributed representation for the new sequenceoutput by step 204 for each word w with the context vector and the wordvector. That is, step 205 runs word2vec or a similar tool on themodified input sequence resulting from the scan above.

Step 206 partitions the new items into classes. That is, step 206 formsclasses, using a clustering algorithm, based on the resulting word (Syn0in w2v) vectors. Step 206 obtain vectors and partitions them intoclasses via clustering.

As shown more specifically in FIG. 4, step 207 displays dominant membersof each class. For each original w, the display step 207 displays itsw_i variations dominant members next to each other. More specifically,step 207 presents for each vocabulary word wi, for each “variation”wi_j, the class to which it belongs by listing the “heaviest”, forexample, 8 members. Again, “heaviest” can be defined in various ways asdiscussed above.

In view of the foregoing and other problems, disadvantages, anddrawbacks of the aforementioned background art, the disclosed inventionabove can provide a new and improved determining and discerning itemswith multiple meanings system which can determine and discern items withmultiple meanings. More particularly, it is desirable to provide anunsupervised and non-dictionary based system, method, and recordingmedium for determining “senses” in which a word appears and associatingthe senses with particular occurrences of the word in the inputsequence.

An exemplary aspect of the disclosed invention provides a system,method, and non-transitory recording medium for determining anddiscerning items with multiple meanings which can provide a technicalsolution to the technical problem in the conventional approaches byexamining the distributed representation of words in the sequence,deciding on main types of occurrences, and then, replacing eachoccurrence of the word in the input sequence with the “appropriatesense” word and occurrence sequence by listing “strongly related” wordsfor each of the occurrences. That is, an exemplary aspect of thedisclosed invention can provide a technical solution that two trainings,one over the original text and one over the modified text in which eachword is replaced by its “sense” so as to explain the ‘senses’ using thetext itself to discern between words having multiple meanings

In this manner, the disclosed invention can provide a technical solutionto the technical problem in the conventional approaches and each wordcan be given a definition in the sequence to determine and discern theitems with multiple meanings. Further, the new and improved determiningand discerning items with multiple meanings system is based on vectorssuch that no auxiliary structures such as dictionaries are needed andthe senses are not defined in advance and are actually discovered andcharacterized by listing the words having them.

Exemplary Hardware Aspects, Using a Cloud Computing Environment

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 5, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 5, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 6, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 8 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 7, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 6) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 7 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and, more particularly relative to thepresent invention, the determining and discerning items with multiplemeanings system 100 described herein.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

Further, Applicant's intent is to encompass the equivalents of all claimelements, and no amendment to any claim of the present applicationshould be construed as a disclaimer of any interest in or right to anequivalent of any element or feature of the amended claim.

What is claimed is:
 1. A method of determining and discerning items withmultiple meanings in a sequence of items, the method comprising:producing a distributed representation for each item of the sequence ofitems including a word vector and a context vector; partitioning thesequence of items into classes; for an item using a representative wordvector of each class, calculating a cosine distance between the wordvector of said item and the class representative vector; and producing anew sequence of items by modifying the distributed representation in theproducing by replacing each occurrence of an item in the distributedrepresentation having a largest cosine distance between the word vectorand the class representative vector calculated by the calculating; andwherein the producing the new sequence produces the new sequence byreplacing each word occurrence in the input sequence of items by w_i,where i comprises the class index among closest classes that the cosinedistance of the representative context vector of the class is closest toan average word vector for the items in a window around the item.
 2. Themethod of claim 1, further comprising producing a new distributedrepresentation for the new sequence of items produced in the producingthe new sequence of items.
 3. The method of claim 2, further comprisingpartitioning the set of items in the new sequence of items into classes.4. The method of claim 2, further comprising displaying a dominantmember of each class based on the member having the largest cosinedistance between the word vector of a potential dominant member item andthe class representative vector.
 5. The method of claim 1, wherein thecalculating scans the sequence of items and sets a current center itemas a vocabulary word and uses an average of the word vectors of items ina window of a predetermined size surrounding the vocabulary word, andthen calculates the cosine distance of the vector average with a classrepresentative context vector.
 6. The method of claim 1, wherein theproducing the new sequence replaces the item with a variation of theitem based on a sense of the item in a current context usage.
 7. Themethod of claim 1, wherein the producing the new sequence replaces theitem with a variation of the item based on a sense of the item in acurrent context usage.
 8. The method of claim 1, wherein a first phaseincludes the producing the distributed representation, the partitioning,and the calculating the cosine distance, the method further comprising:in a second phase when the producing produces the new sequence:producing a modified sequence of items by replacing each item Ioccurrence in the sequence by an I_j occurrence where j is one of the Dclasses for item I; producing a new distributed representation for eachitem I_m of the modified sequence of items including a word vector and acontext vector; and using said new distributed representation fordetermining and discerning items with multiple meanings.
 9. The methodof claim 1, further comprising producing for each item by calculating afixed number of classes D with smallest said cosine distance.
 10. Anon-transitory computer-readable recording medium recording a programfor determining and discerning items with multiple meanings in asequence of items, the program causing a computer to perform: producinga distributed representation for each item of the sequence of itemsincluding a word vector and a context vector; partitioning the sequenceof items into classes; using a representative word vector of each class,calculating a cosine distance between the word vector and the classrepresentative word vector; and producing a new sequence of items bymodifying the distributed representation in the producing by replacingeach occurrence of an item in the distributed representation having alargest cosine distance between the word vector and the classrepresentative vector calculated by the calculating; and wherein theproducing the new sequence produces the new sequence by replacing eachword occurrence in the input sequence of items by w_i, where i comprisesthe class index among closest classes that the cosine distance of therepresentative context vector of the class is closest to an average wordvector for the items in a window around the item.
 11. The non-transitorycomputer-readable recording medium of claim 10, further comprisingproducing a new distributed representation for the new sequence of itemsproduced in the producing the new sequence of items.
 12. Thenon-transitory computer-readable recording medium of claim 11, furthercomprising partitioning the set of items in the new sequence of itemsinto classes.
 13. The non-transitory computer-readable recording mediumof claim 11, further comprising displaying a dominant member of eachclass based on the cosine distance between the word vector of apotential dominant member item and the class representative vector. 14.The non-transitory computer-readable recording medium of claim 11,wherein the calculating scans the sequence of items and sets a currentcenter item as a vocabulary word and uses an average of the word vectorsof items in a window of a predetermined size surrounding the vocabularyword and then calculates the cosine distance of the vector average witha class representative context vector.
 15. A system for determining anddiscerning items with multiple meanings in a sequence of items, thesystem comprising: a first distribution producing device configured toproduce a distributed representation for each item of the sequence ofitems including a word vector and a context vector; a first partitioningdevice configured to partition the sequence of items into classes; acloseness determining device configured to calculate, using arepresentative word vector of each class, a cosine distance between theword vector and the class representative word vector; and a new sequenceproducing device configured to produce a new sequence of items bymodifying the distributed representation in the producing by replacingeach occurrence of an item in the distributed representation having alargest cosine distance between the word vector and the classrepresentative vector calculated by the calculating; and wherein theproducing the new sequence produces the new sequence by replacing eachword occurrence in the input sequence of items by w_i, where i comprisesthe class index among closest classes that the cosine distance of therepresentative context vector of the class is closest to an average wordvector for the items in a window around the item.
 16. The system ofclaim 15, further comprising a second distribution representationproducing device configured to produce a new distributed representationfor the new sequence of items produced in the producing the new sequenceof items.
 17. The system of claim 15, further comprising a secondpartitioning device configured to partition the new sequence of itemsinto classes.
 18. The system of claim 15, further comprising a displaydevice configured to display a dominant member of each class based onthe cosine distance between the word vector and the class representativeword vector.